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Data augmentation for deep-learning-based electroencephalography
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …
considerable performance gains for deep learning (DL)—increased accuracy and stability …
Sleep in Alzheimer's disease: a systematic review and meta-analysis of polysomnographic findings
Y Zhang, R Ren, L Yang, H Zhang, Y Shi… - Translational …, 2022 - nature.com
Polysomnography (PSG) studies of sleep changes in Alzheimer's disease (AD) have
reported but not fully established the relationship between sleep disturbances and AD. To …
reported but not fully established the relationship between sleep disturbances and AD. To …
Insomnia in the elderly: a review
Background: Insomnia remains one of the most common sleep disorders encountered in the
geriatric clinic population, frequently characterized by the subjective complaint of difficulty …
geriatric clinic population, frequently characterized by the subjective complaint of difficulty …
Short-and long-term health consequences of sleep disruption
G Medic, M Wille, MEH Hemels - Nature and science of sleep, 2017 - Taylor & Francis
Sleep plays a vital role in brain function and systemic physiology across many body
systems. Problems with sleep are widely prevalent and include deficits in quantity and …
systems. Problems with sleep are widely prevalent and include deficits in quantity and …
Evaluating reliability in wearable devices for sleep staging
Sleep is crucial for physical and mental health, but traditional sleep quality assessment
methods have limitations. This sco** review analyzes 35 articles from the past decade …
methods have limitations. This sco** review analyzes 35 articles from the past decade …
Uncovering the structure of clinical EEG signals with self-supervised learning
Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …
that is available. This phenomenon is particularly problematic in clinically-relevant data …
Normal polysomnography parameters in healthy adults: a systematic review and meta-analysis
Background Existing normal polysomnography values are not truly normative as they are
based on small sample sizes due to the fact that polysomnography is expensive and …
based on small sample sizes due to the fact that polysomnography is expensive and …
A deep transfer learning approach for wearable sleep stage classification with photoplethysmography
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography
(PPG) could open the way for better sleep disorder screening and health monitoring …
(PPG) could open the way for better sleep disorder screening and health monitoring …
WiFi-sleep: Sleep stage monitoring using commodity Wi-Fi devices
Sleep monitoring is essential to people's health and wellbeing, which can also assist in the
diagnosis and treatment of sleep disorder. Compared with contact-based solutions …
diagnosis and treatment of sleep disorder. Compared with contact-based solutions …
Automatic sleep stage classification: From classical machine learning methods to deep learning
RN Sekkal, F Bereksi-Reguig… - … Signal Processing and …, 2022 - Elsevier
Background and objectives The classification of sleep stages is a preliminary exam that
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …